In addition to classification, MALLET includes tools for sequence tagging
for applications such as named-entity extraction from text.
Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and
Conditional Random Fields. These methods are implemented in an extensible system
for finite state transducers.
[Quick Start]
[Developer's Guide]

Many of the algorithms in MALLET depend on numerical optimization.
MALLET includes an efficient implementation of Limited Memory BFGS,
among many other optimization methods.
[Developer's Guide]

In addition to sophisticated Machine Learning applications,
MALLET includes routines for transforming text documents into
numerical representations that can then be processed efficiently.
This process is implemented through a flexible system of "pipes",
which handle distinct tasks such as tokenizing strings, removing stopwords,
and converting sequences into count vectors.
[Quick Start] [Developer's Guide]

An add-on package to MALLET, called GRMM, contains support for inference in general graphical models,
and training of CRFs with arbitrary graphical structure. [About GRMM]

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The toolkit is Open Source Software, and is released under the
Common Public License.
You are welcome to use the code under the terms of the licence for
research or commercial purposes, however please acknowledge its use
with a citation: